Databases, Features and Classification Techniques for Speech Emotion Recognition
Jasmeet Kaur1, Anil kumar2
1Jasmeet Kaur* , Dept. of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, India.
2Anil Kumar, Dept. of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 185-194 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3487049620/2020©BEIESP | DOI: 10.35940/ijitee.F3487.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Emotion recognition is a rapidly growing research field. Emotions can be effectively expressed through speech and can provide insight about speaker’s intentions. Although, humans can easily interpret emotions through speech, physical gestures, and eye movement but to train a machine to do the same with similar preciseness is quite a challenging task. SER systems can improve human-machine interaction when used with automatic speech recognition, as emotions have the tendency to change the semantics of a sentence. Many researchers have contributed their extremely impressive work in this research area, leading to development of numerous classification, feature selection, feature extraction and emotional speech databases. This paper reviews recent accomplishments in the area of speech emotion recognition. It also present a detailed review of various types of emotional speech databases, and different classification techniques which can be used individually or in combination and a brief description of various speech features for emotion recognition.
Keywords: Emotion Recognition, Classification Models, Emotional Speech Databases, Prosodic Features, Excitation Source Features, Spectral Feature.
Scope of the Article: Image Processing and Pattern Recognition